High-quality Community Research Articles

Community Detection Algorithm for Big Social Networks Using Hybrid Architecture

Abstract One of the most relevant and widely studied structural properties of networks is their community structure. Detecting communities is of great importance in social networks where systems are often represented as graphs. With the advent of web-based social networks like Twitter, Facebook and LinkedIn. community detection became even more difficult due to the massive network size, which can reach up to hundreds of millions of vertices and edges. This large graph structured data cannot be processed without using distributed algorithms due to memory constraints of one machine and also the need to achieve high performance. In this paper, we present a novel hybrid (shared + distributed memory) parallel algorithm to efficiently detect high quality communities in massive social networks. For our simulations, we use synthetic graphs ranging from 100K to 16M vertices to show the scalability and quality performance of our algorithm. We also use two massive real world networks: (a) section of Twitter-2010 network having ≈ 41 M vertices and ≈ 1.4 B edges (b) UK-2007 (.uk web domain) having ≈ 105 M vertices and ≈ 3.3 B edges. Simulation results on MPI setup with 8 compute nodes having 16 cores each show that, upto ≈ 6 X speedup is achieved for synthetic graphs in detecting communities without compromising the quality of the results.

Label propagation algorithm for community detection based on node importance and label influence

Recently, the detection of high-quality community has become a hot spot in the research of social network. Label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity and is unnecessary to define objective function and the number of community in advance. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the community. For large-scale social network, this paper proposes a novel label propagation algorithm for community detection based on node importance and label influence (LPA_NI). The experiments with comparative algorithms on real-world networks and synthetic networks have shown that LPA_NI can significantly improve the quality of community detection and shorten the iteration period. Also, it has better accuracy and stability in the case of similar complexity.

Recognition through reconnaissance? Using drones for counter-mapping in Indonesia

Indonesia has a long history of land grabs before this term was coined, reaching from colonial occupation to cleptocratic rule. The most recent wave of enclosures across the archipelago builds on large-scale, market-oriented spatial planning. This paper shares our experience of using unmanned aerial vehicles (drones) to produce high-quality community controlled maps in order to challenge some of the official spatial planning processes in West Kalimantan. Developed at first as a component of action research looking at the political ecology of the Kapuas River, the drone mapping soon developed its own dynamics and delivered quite impressive results in bolstering legal and political claims of Non-Governmental Organisations (NGOs) and Dayak communities. We argue that relatively simple and accessible drone technology has some potential for furthering the recognition of local and indigenous people and their territorial claims. Such a view, however, stands in contrast to recent debates that have highlighted the limits and even detrimental social and political effects of counter-mapping. Drawing on our experience with ‘community drones’, we compare ‘traditional’ and drone counter-mapping in key dimensions of production, distribution and use. This comparison helps to delimit more clearly the occasions and conditions under which drone-based counter-mapping may be a politically useful tool.

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.

Efficient Methods for Influence-Based Network-Oblivious Community Detection

We study the problem of detecting social communities when the social graph is not available but instead we have access to a log of user activity, that is, a dataset of tuples ( u , i , t ) recording the fact that user u “adopted” item i at time t . We propose a stochastic framework that assumes that the adoption of items is governed by an underlying diffusion process over the unobserved social network and that such a diffusion model is based on community-level influence . That is, we aim at modeling communities through the lenses of social contagion . By fitting the model parameters to the user activity log, we learn the community membership and the level of influence of each user in each community. The general framework is instantiated with two different diffusion models, one with discrete time and one with continuous time, and we show that the computational complexity of both approaches is linear in the number of users and in the size of the propagation log. Experiments on synthetic data with planted community structure show that our methods outperform non-trivial baselines. The effectiveness of the proposed techniques is further validated on real-word data, on which our methods are able to detect high-quality communities.

Causes of death in children younger than five years in China in 2015: an updated analysis.

BackgroundSubstantial progress in reducing the child mortality rate has been made globally in the last two decades. However, for China, the number of children dying from preventable diseases is still very large. It is important to have regularly updated information on the distribution of causes of death (COD) in children to inform policy and research. In this study, we aim to estimate the COD spectrum in children younger than five years old from 2009 to 2015 with a focus on the year 2015 and to provide an updated COD prediction model for China.MethodsUpdated data of under–five mortality rates (U5MRs) and the number of live births at national and provincial levels were obtained from United Nation's Inter–agency Group for Child Mortality Estimation (UN IGME), Institute for Health Metrics and Evaluation (IHME), and United Nations Population Division (UNPD). Then, we conducted a systematic review across four Chinese and English bibliographic databases and identified high–quality community–based longitudinal studies of COD in children younger than five years in China. We developed a number of single–cause models to predict the number of child death for main COD in different age groups at both national and provincial levels. The jackknife procedure was applied to construct the 95% Uncertainty Ranges (URs).ResultsFrom 2009 to 2015, the under–five mortality rates have declined by 37.1%. The leading causes of death in 2015 were preterm birth complications (17.4%), birth asphyxia (15.2%), congenital abnormalities (14.1%), accidents (13.5%) and pneumonia (12.4%) for children under five years old. The COD spectrum varied substantially across Chinese provinces with different development levels. The leading cause in children under five years in the wealthier provinces (with lower U5MRs) was congenital abnormalities (up to 18.9%), while in the poorer provinces (with higher U5MRs), pneumonia was the dominant COD (up to 23.4%).ConclusionsThis study updates and validates the accuracy of the findings of our previous COD study and proposes a new modelling method to predict proportions for the most common causes of child death in China. These updated COD estimates suggest that current strategies to reduce child mortality should prioritise action on neonatal deaths and target interventions against the top COD according to the local COD spectrum. Special attention should also be given to reducing differences between Chinese provinces and regions with differing development levels.

Tracking local communities in streaming graphs with a dynamic algorithm

A variety of massive datasets, such as social networks and biological data, are represented as graphs that reveal underlying connections, trends, and anomalies. Community detection is the task of discovering dense groups of vertices in a graph. Its one specific form is seed set expansion, which finds the best local community for a given set of seed vertices. Greedy, agglomerative algorithms, which are commonly used in seed set expansion, have been previously designed only for a static, unchanging graph. However, in many applications, new data are constantly produced, and vertices and edges are inserted and removed from a graph. We present an algorithm for dynamic seed set expansion, which maintains a local community over time by incrementally updating as the underlying graph changes. We show that our dynamic algorithm outputs high-quality communities that are similar to those found when using a standard static algorithm. It works well both when beginning with an already existing graph and in the fully streaming case when starting with no data. The dynamic approach is also faster than re-computation when low latency updates are needed.

Abstract 1787A: Puerto Rico BioBank: The first cancer tissue biobank at a US Hispanic-serving institution

Abstract There are currently 580 million people worldwide considered to be of Hispanic origin. In the US Hispanics represent 17% of the population and its largest ethnic minority. Funded by NIH, Moffitt Cancer Center (Florida) and Ponce School of Medicine (Puerto Rico) initiated a partnership that conducts high-quality research, training, and community outreach focusing on cancer health disparities among Hispanics. A central component to this partnership is the establishment of the first cancer-focused biobank in the island, the Puerto Rico BioBank (PRBB). The PRBB has actively recruited cancer patients since 2009; over these years we have established essential collaborations and a functional infrastructure. Eligible cancer patients provide informed consent and agree to donate blood samples (for DNA) and solid tissue(fresh-frozen & paraffin embedded). Additionally, patients complete a comprehensive questionnaire related to clinical and lifestyle factors. Biospecimen collection at the PHSU follow standard operating procedures that mirror and often exceed NCI biobaking best practices for biospecimens and ensuring patient data confidentiality. Currently, the PRBB has over 1,100 consented patients (65.8% females, 34.2% males) all of Hispanic origin. The most common banked tumors are Breast, Prostate, Lung and Endometrium, although other tumors are represented. All the information and data are entered into a biospecimen management system (BMS) developed by the partnership, which allows for real-time biospecimen inventory management and reporting functionality. In conclusion, the Puerto Rican patient samples banked in the PRBB are a unique resource that can support multiple molecular and population studies. Also, a Hispanic-specific biobank can foster collaborations among local and international researchers that may facilitate the application of novel molecular techniques to solving cancer health disparities among Hispanics. Citation Format: Patricia Casbas-Hernandez, Idhaliz Flores, Edward Seijo, Domenico Coppola, Steve Eschrich, Rodrigo Carvajal-Pelaez, Sonia Abac, Dagmar Correa, Edna Gordian, Teresita Munoz-Antonia. Puerto Rico BioBank: The first cancer tissue biobank at a US Hispanic-serving institution. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1787A.

Community nursing quality indicators for end-of-life care in England: identification, preparation, and coordination.

High-quality community nursing is essential to ensure that end-of-life care can be provided in community settings in line with patient preferences. This article examines the quality priorities commissioners sought to incentivise in end-of-life care, by reviewing a survey of Commissioning for Quality and Innovation (CQUIN) indicators for community nursing conducted in England in 2014-2015. Findings from the survey suggest that end-of-life care was not given a high priority with the CQUIN indicators for community nursing. Vigorous quality standards, including training and development, need to be in place to make sure that the potential of community nursing is being used to sensitively engage with people nearing the end of their lives and support them to plan their future care, if they so wish.

A divisive spectral method for network community detection

Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others.

Fast community detection based on sector edge aggregation metric model in hyperbolic space

By studying the edge aggregation characteristic of nodes in hyperbolic space, Sector Edge Aggregation Metric (SEAM) model is proposed and theoretically proved in this paper. In hyperbolic disk SEAM model determines the minimum angular range of a sector which possesses the maximal edge aggregation of nodes. The set of nodes within such sector has dense internal links, which corresponds with the characteristic of community structure. Based on SEAM model, we propose a fast community detection algorithm called Greedy Optimization Modularity Algorithm (GOMA) which employs greedy optimization strategy and hyperbolic coordinates. GOMA firstly divides initial communities according to the quantitative results of sector edge aggregation given by SEAM and the nodes’ hyperbolic coordinates, then based on greedy optimization strategy, only merges the two angular neighboring communities in hyperbolic disk to optimize the network modularity function, and consequently obtains high-quality community detection. The strategies of initial community partition and merger in hyperbolic space greatly improve the speed of searching the most optimal modularity. Experimental results indicate that GOMA is able to detect out high-quality community structure in synthetic and real networks, and performs better when applied to the large-scale and dense networks with strong clustering.

Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

Ego-net community mining applied to friend suggestion

In this paper, we present a study of the community structure of ego-networks---the graphs representing the connections among the neighbors of a node---for several online social networks. Toward this goal, we design a new technique to efficiently build and cluster all the ego-nets of a graph in parallel (note that even just building the ego-nets efficiently is challenging on large networks). Our experimental findings are quite compelling: at a microscopic level it is easy to detect high quality communities. Leveraging on this fact we, then, develop new features for friend suggestion based on co-occurrences of two nodes in different ego-nets' communities. Our new features can be computed efficiently on very large scale graphs by just analyzing the neighborhood of each node. Furthermore, we prove formally on a stylized model, and by experimental analysis that this new similarity measure outperforms the classic local features employed for friend suggestions.

SIMPLE: a simplifying-ensembling framework for parallel community detection from large networks

Community detection is a classic and very difficult task in complex network analysis. As the increasingly explosion of social media, scaling community detection methods to large networks has attracted considerable recent interests. In this paper, we propose a novel SIMPLifying and Ensembling (SIMPLE) framework for parallel community detection. It employs the random link sampling to simplify the network and obtain basic partitionings on every sampled graphs. Then, the K-means-based Consensus Clustering is used to ensemble a number of basic partitionings to get high-quality community structures. All of phases in SIMPLE, including random sampling, sampled graph partitioning, and consensus clustering, are encapsulated into MapReduce for parallel execution. Experiments on six real-world social networks analyze key parameters and factors inside SIMPLE, and demonstrate both effectiveness and efficiency of the SIMPLE.

Community nursing: the linchpin of quality care.

❛If community nursing is to be recognised as the linchpin of high-quality care outside hospitals, the invisibility of community nurses needs to be reversed ...❜ Before the modern era of pharmaceutics and other medical advances, the GP and community health services used to constitute the mainstay of people’s health-care experience. However, in recent years, hospital care has become pre-eminent to the neglect of the breadth and sophistication of current out-of-hospital services, with the consequence that many patients view nonhospital care as inferior, if not second best. There is a tendency for patients to equate self-sufficiency and self-care with appropriate local support as an absence of needed health-care support, because opportunities to reassure the general public, and patients in particular, and to signpost access to high-quality community services have not been grasped. Indeed, all too frequently, various health-care users are reported in the media as being ‘forgotten’, or worse ‘neglected’, by a hard-pressed health service. Davies et al’s (2015) qualitative study of 20 people with multiple sclerosis (MS) and 13 carers highlights the importance of health-care support in the lives of patients as they transition to secondary progressive MS. They summarised the patient experience as: ‘You are just left to get on with it’, in the absence of being adequately informed by their health-care team. The authors’ study sample wanted to know what secondary progressive MS would mean for them including increased disability and its consequences regarding employment and other roles and what they should and could do for themselves. They also wanted to know what health-care services could do for them, particularly in terms of potential treatments; the management of symptoms such as fatigue, pain, low mood, personality, and cognitive changes; and what future support they may expect. The infrequency of reported health-care contacts and lack of carer support were noteworthy as was the failure to convey a confirmation that there would be a supportive network that would include community nurses as the disease progresses. If community nursing is to be recognised as the linchpin of high-quality care outside hospitals, the invisibility of community nurses needs to be reversed and the breadth and scope of their work advertised, both by community nurses themselves and their primary care colleagues. The Queen Nursing Institute’s (QNI’s) (2014) 2020 Vision 5 Years On summarised the recent ‘bumpy’ history of district nursing since the early 2000s, with the challenges of clinical grading, confusing job titles, new specialisms, and major organisational changes. Disappointingly the report noted that widespread confusion continues relating to the district nurse job title, qualifications, and roles, with some of the QNI survey respondents reporting despair at the uncontrolled use of the ‘district nurse’ title. The current multiplicity of workload-management systems highlights the need for an intelligent and robust demand and capacity-management tool to enable community nurses to deliver care to the ‘right’ patients at the ‘right’ time, thereby enabling community nurses to provide appropriate support within the constraint of resources. The QNI’s championing of the role of nurses as the linchpins for the avoidance of unnecessary hospital admissions and delivery of high-quality care in the home needs community nurses themselves to inform the public of their nursing contribution. This will require community nurses not only to articulate the outcomes of their care delivery, but also ensure that other primary care staff, such as GPs and pharmacists, ‘sing their praises’ alongside patients and carers. This is not the time for community nurses to be invisible. BJCN

Substantial between-country differences in organising community care for older people in Europe-a review.

The European population is aging. The main drivers of public spending on health care for people of 65 years and older are hospital admission and admission to long-term care facilities. High quality community care can be a cost-effective and quality solution to respond to the impact of ageing populations on health-care systems. It is unclear how well countries are equipped to provide affordable and quality community care. The aim of this article is to describe and compare community care delivery with care-dependent older people in Europe. This study is conducted within the European Union-financed IBenC project [Identifying best practices for care-dependent elderly byBenchmarkingCosts and outcomes of community care (FP7)] in which six European countries are involved. To compare the community care delivery with care-dependent older people in these countries, we performed a systematic comparison of macro indicators using metadata complemented with data from multinational surveys. Data on the following dimensions are described and compared: population of the country, governmental expenditures on health, sources of community health services funding, governmental vision and regulation on community care, community care organisations and care professionals, eligibility criteria for and equity in receiving care and the involvement of informal care. : Because of the variations in the European community care contexts, the growing demand for community care as a cost-effective and quality solution to the care burden of aging populations will have country-specific impacts. When learning from other countries' best practices, in addition to researchers, policy makers should take full account of local and national care contexts.

Successful recommissioning of community diabetes services in West Hampshire

Introduction: The West Hampshire Community Diabetes Service replaced traditional hospital-based diabetes outpatients in 2010 and has been successfully recommissioned, one of the first community diabetes services to complete the commissioning cycle. Method: The successful introduction of traditional diabetes services into the community requires clear leadership and a strategic vision. Unlike many hospital-based services, the community setting is at the frontline of Clinical Commissioning Group (CCG) targets for recommissioning. Thus, the threat of change is relentless and must be embraced rather than feared. As a result, the community setting can offer a new approach to the management of long-term conditions, with healthier and more productive partnerships between healthcare professionals and patients. We have identified four success criteria: skills for leadership, strategic vision, stakeholder partnerships, and shared learning.Results: The success of the diabetes service redesign in West Hampshire is reflected in successful recommissioning of the service by the West Hampshire CCG. This is one of the first such services to be successfully recommissioned in the country.Conclusions: Relocating general diabetes services into the community has been a catalyst for improved integration between specialist and primary care in West Hampshire. The process of service redesign and recommissioning is not widely communicated between consultant diabetologists, who will be pivotal to developing and delivering high-quality community care. Communicating these messages to doctors in training will ensure newly-appointed consultants have the skills required to lead change in local diabetes services and ensure high performing services are recommissioned successfully.

An evaluation of a behavioural support team for adults with a learning disability and behaviours that challenge from a multi-agency perspective

Accessible Summary Dudley Behaviour Support Team aims to help make areas of life better for people with behaviours that challenge. It also aims to help their carers support them better. This project looked at whether the Behaviour Support Team is meeting guidelines set out by the government to ensure services are doing their job well. Staff who work with the Behaviour Support Team were asked to take part in the study by answering questions about how well the team does. This study found that the Behaviour Support Team is meeting the government guidelines to a high standard. Summary The Dudley Behavioural Support Team (BST) was set up based on Positive Behavioural Support (PBS) principles to support individuals with behaviours that challenge. The Winterbourne Review emphasises the importance of developing high-quality specialist community services and the Ensuring Quality Services (Local Government Association & NHS England, Ensuring quality services, London, 2014) document was released to guide the commissioning of such services. The document sets out standards and guidance for those supporting individuals with behaviour that challenges. This evaluation aimed to investigate whether the BST meets the guidance set out in Ensuring Quality Services from the perspective of other services. A questionnaire was developed to collect data from 19 staff members from services with experience of working with the BST, to address the points raised in Ensuring Quality Services. The quantitative results indicated that the BST are meeting the guidance to a high standard and qualitative data suggested this is achieved through a focus on joint working and a fluid, flexible approach. In conclusion, the BST appears to be a valuable resource from the perspective of services working with them to support individuals with behaviours that challenge.

Local Community Mining on Distributed and Dynamic Networks From a Multiagent Perspective

Distributed and dynamic networks are ubiquitous in many real-world applications. Due to the huge-scale, decentralized, and dynamic characteristics, the global topological view is either too hard to obtain or even not available. So, most existing community detection methods working on the global view fail to handle such decentralized and dynamic large networks. In this paper, we propose a novel autonomy-oriented computing-based method for community mining (AOCCM) from the multiagent perspective in the distributed environment. In particular, AOCCM utilizes reactive agents to pick the neighborhood node with the largest structural similarity as the candidate node, and thus determine whether it should be added into local community based on the modularity gain. We further improve AOCCM to a more efficient incremental version named AOCCM-i for mining communities from dynamic networks. AOCCM and AOCCM-i can be easily expanded to detect both nonoverlapping and overlapping global community structures. Experimental results on real-life networks demonstrate that the proposed methods can reduce the computational cost by avoiding repeated structural similarity calculation and can still obtain the high-quality communities.

Improving Community Detection in Time-Evolving Networks Through Clustering Fusion

Abstract Traditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to improve the results of community detection. Usually, there are two issues in clustering ensembles: how to generate efficient diversified cluster members, and how to ensembles the results of all members. Specifically: (1) considering the time evolving characteristic of real world networks, we propose to generate clustering members based on the snapshot of networks, where the split based clustering algorithms are performed; (2) considering the difference in the distribution of the cluster centers in each clustering member and the actual distribution, we ensemble the results based on a maximum likelihood method. Moreover, we conduct experiments to show that our method can discover high quality communities.